Hierarchical large-margin Gaussian mixture models for phonetic classification

In this paper we present a hierarchical large-margin Gaussian mixture modeling framework and evaluate it on the task of phonetic classification. A two-stage hierarchical classifier is trained by alternately updating parameters at different levels in the tree to maximize the joint margin of the overall classification. Since the loss function required in the training is convex to the parameter space the problem of spurious local minima is avoided. The model achieves good performance with fewer parameters than single-level classifiers. In the TIMIT benchmark task of context-independent phonetic classification, the proposed modeling scheme achieves a state-of-the-art phonetic classification error of 16.7% on the core test set. This is an absolute reduction of 1.6% from the best previously reported result on this task, and 4-5% lower than a variety of classifiers that have been recently examined on this task.